Abstract
In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Wolfe, C.: The impact of stroke. Br. Med. Bull. 56(2), 275–286 (2000)
Hogan, N., Krebs, H., Rohrer, B., Palazzolo, J., Dipietro, L., Fasoli, S., Stein, J., Hughes, R., Frontera, W., Lynch, D., et al.: Motions or muscles? some behavioral factors underlying robotic assistance of motor recovery. J. Rehabil. Res. Dev. 43(5), 605 (2006)
Riener, R., Lünenburger, L., Colombo, G.: Human-centered robotics applied to gait training and assessment. J. Rehabil. Res. Dev. 43(5), 679–694 (2006)
Koh, C., Hoffmann, T., Bennett, S., McKenna, K.: Management of patients with cognitive impairment after stroke: A survey of australian occupational therapists. Aust. Occup. Ther. J. 56(5), 324–331 (2009)
He, W., Ge, S.S., Li, Y., Chew, E., Ng, Y.S.: Impedance control of a rehabilitation robot for interactive training, In Proceedings of the 2012 International Conference on Social Robotics, Chengdu, China, pp. 526–535 (2012)
Yang, C., Ganesh, G., Haddadin, S., Parusel, S. , Albu-Schaeffer, A., Burdet, E.: Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans. Robot. 27(5), 918–930 (2011)
Li, Z., Ge, S.S., Ming, A.: Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(3), 607–616 (2007)
Li, Z., Li, J., Kang, Y.: Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments. Automatica 46(12), 2028–2034 (2010)
Li, Y., Ge, S.S., Yang, C.: Learning impedance control for physical robot–environment interaction. Int. J. Control 85(2), 182–193 (2012)
Hogan, N.: Impedance control: An approach to manipulation: Part iii applications. J. Dyn. Syst. Meas., control 107(2), 1–24 (1985)
Huang, L., Ge, S.S., Lee, T.H.: Position/force control of uncertain constrained flexible joint robots. Mechatronics 16(2), 111–120 (2006)
Li, Z., Ge, S.S., Adams, M., Wijesoma, W.: Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators. Automatica 44(3), 776–784 (2008)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)
Levin, A.U., Narendra, K.S.: Control of nonlinear dynamical systems using neural networks. ii. observability, identification, and control. IEEE Trans. Neural Netw. 7(1), 30–42 (1996)
Ge, S.S., Lee, T.H., Harris, C.J.: Adaptive Neural Network Control of Robotic Manipulators. London, UK: World Scientific (1998)
Lewis, F.L., Jagannathan, S., Yeildirek, A.: Neural network control of robot manipulators and nonlinear systems. Taylor & Francis, London (1999)
Wang, C., Hill, D.J.: Deterministic learning theory for identification, recognition, and control. CRC Press (2009)
Li, Y., Yang, C., Ge, S.S., Lee, T.H.: Adaptive output feedback nn control of a class of discrete-time MIMO nonlinear systems with unknown control directions. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 41(2), 507–517 (2011)
Ren, B., Ge, S.S., Su, C.-Y., Lee, T.H.: Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 39(2), 431–443 (2009)
Chen, M., Ge, S.S., Ren, B.: Adaptive tracking control of uncertain mimo nonlinear systems with input constraints. Automatica 47(3), 452–465 (2011)
Liu, Y.-J., Chen, C.P., Wen, G.-X., Tong, S.: Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems. IEEE Trans. Neural Netw. 22(7), 1162–1167 (2011)
Liu, Y.-J., Tong, S.-C., Wang, D., Li, T.-S., Chen, C.: Adaptive neural output feedback controller design with reduced-order observer for a class of uncertain nonlinear siso systems. IEEE Trans. Neural Netw. 22(8), 1328–1334 (2011)
Dai, S.-L., Wang, C., Wang, M.: Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 111–123 (2014)
Dai, S.-L., Wang, C., Luo, F.: Identification and learning control of ocean surface ship using neural networks. IEEE Trans. Ind. Inform. 8(34), 801–810 (2012)
Li, Z., Su, C.-Y.: Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties. IEEE Trans. Neural Netw. Learn. Syst. 24(9), 1400–1413 (2013)
Sun, F., Sun, Z., Zhu, Y., Lu, W.: Stable neuro-adaptive control for robots with the upper bound estimation on the neural approximation errors. J. Intell. & Robot. Syst. 26(1), 91–100 (1999)
Sun, F., Sun, Z.: Stable sampled-data adaptive control of robot arms using neural networks. J. Intell. & Robot. Syst. 20(2-4), 131–155 (1997)
He, W., Ge, S.S., How, B.V.E., Choo, Y.S.: Dynamics and Control of Mechanical Systems in Offshore Engineering (2014)
Selmic, R., Lewis, F.: Deadzone compensation in motion control systems using neural networks. IEEE Trans. Autom. Control 45(4), 602–613 (2000)
Yang, C., Li, Z., Li, J.: Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models. IEEE Trans. Cybern. 43(1), 24–36 (2013)
Cui, R., Ren, B., Ge, S.S.: Synchronised tracking control of multi-agent system with high order dynamics. IET Control Theory & Appl. 6(5), 603–614 (2012)
Sanner, R.M., Slotine, J.E.: Gaussian networks for direct adaptive control. IEEE Trans. Neural Netw. 3(6), 837–863 (1992)
Ge, S.S., Wang, C.: Adaptive neural network control of uncertain MIMO non-linear systems. IEEE Trans. Neural Netw. 15(3), 674–692 (2004)
Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Kluwer Academic, Boston, USA (2001)
Behtash, S.: Robust output tracking for nonlinear system. Int. J. Control 51, 1381–1407 (1990)
Horn, R., Johnson, C.: Matrix analysis. Cambridge University Press, Cambridge UK (1990)
He, W., Ge, S.S.: Robust adaptive boundary control of a vibrating string under unknown time-varying disturbance. IEEE Trans. Control Syst. Technol. 20(1), 48–58 (2012)
He, W., Ge, S.S., How, B.V.E., Choo, Y.S., Hong, K.-S.: Robust adaptive boundary control of a flexible marine riser with vessel dynamics. Automatica 47(4), 722–732 (2011)
Ioannou, P., Sun, J.: Robust Adaptive Control. Prentice-Hall, Eaglewood Cliffs, New Jersey (1996)
He, W., Ge, S.S., Zhang, S.: Adaptive boundary control of a flexible marine installation system. Automatica 47(12), 2728–2734 (2011)
He, W., Zhang, S., Ge, S.S.: Robust adaptive control of a thruster assisted position mooring system. Automatica 50(7), 1843–1851 (2014)
Khalil, H.K., Systems Nonlinear. Prentice Hall, New Jersey USA (2002)
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China under Grant 61203057, the Fundamental Research Funds for the China Central Universities of UESTC under Grant ZYGX2013Z003, and the National Basic Research Program of China (973 Program) under Grant 2014CB744206
Rights and permissions
About this article
Cite this article
He, W., Ge, S.S., Li, Y. et al. Neural Network Control of a Rehabilitation Robot by State and Output Feedback. J Intell Robot Syst 80, 15–31 (2015). https://doi.org/10.1007/s10846-014-0150-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-014-0150-6